Abstract: Text data mining is a process of exploratory data
analysis. Classification maps data into predefined groups or classes.
It is often referred to as supervised learning because the classes are
determined before examining the data. This paper describes proposed
radial basis function Classifier that performs comparative crossvalidation
for existing radial basis function Classifier. The feasibility
and the benefits of the proposed approach are demonstrated by means
of data mining problem: direct Marketing. Direct marketing has
become an important application field of data mining. Comparative
Cross-validation involves estimation of accuracy by either stratified
k-fold cross-validation or equivalent repeated random subsampling.
While the proposed method may have high bias; its performance
(accuracy estimation in our case) may be poor due to high variance.
Thus the accuracy with proposed radial basis function Classifier was
less than with the existing radial basis function Classifier. However
there is smaller the improvement in runtime and larger improvement
in precision and recall. In the proposed method Classification
accuracy and prediction accuracy are determined where the
prediction accuracy is comparatively high.
Abstract: Misalignment and unbalance are the major concerns
in rotating machinery. When the power supply to any rotating system
is cutoff, the system begins to lose the momentum gained during
sustained operation and finally comes to rest. The exact time period
from when the power is cutoff until the rotor comes to rest is called
Coast Down Time. The CDTs for different shaft cutoff speeds were
recorded at various misalignment and unbalance conditions. The
CDT reduction percentages were calculated for each fault and there
is a specific correlation between the CDT reduction percentage and
the severity of the fault. In this paper, radial basis network, a new
generation of artificial neural networks, has been successfully
incorporated for the prediction of CDT for misalignment and
unbalance conditions. Radial basis network has been found to be
successful in the prediction of CDT for mechanical faults in rotating
machinery.
Abstract: The vibrations produced by a single point defect on
various parts of the bearing under constant radial load are predicted
by using a theoretical model. The model includes variation in the
response due to the effect of bearing dimensions, rotating frequency
distribution of load. The excitation forces are generated when the
defects on the races strike to rolling elements. In case of the outer
ring defect, the pulses generated are with periodicity of outer ring
defect frequency where as for inner ring defect, the pulses are with
periodicity of inner ring defect frequency. The effort has been carried
out in preparing the physical model of the system. Different defect
frequencies are obtained and are used to find out the amplitudes of
the vibration due to excitation of the bearing parts. Increase in the
radial load or severity of the defect produces a significant change in
bearing signature characteristics.
Abstract: Dilated cardiomyopathy (DCM) is a severe
cardiovascular disorder characterized by progressive systolic
dysfunction due to cardiac chamber dilatation and inefficient
myocardial contractility often leading to chronic heart failure.
Recently, a genome-wide association studies (GWASs) on DCM
indicate that the ZBTB17 gene rs10927875 single nucleotide
polymorphism is associated with DCM. The aim of the study was to
identify the distribution of ZBTB17 gene rs10927875 polymorphism
in 50 Slovak patients with DCM and 80 healthy control subjects
using the Custom Taqman®SNP Genotyping assays. Risk factors
detected at baseline in each group included age, sex, body mass
index, smoking status, diabetes and blood pressure. The mean age of
patients with DCM was 52.9±6.3 years; the mean age of individuals
in control group was 50.3±8.9 years. The distribution of investigated
genotypes of rs10927875 polymorphism within ZBTB17 gene in the
cohort of Slovak patients with DCM was as follows: CC (38.8%), CT
(55.1%), TT (6.1%), in controls: CC (43.8%), CT (51.2%), TT
(5.0%). The risk allele T was more common among the patients with
dilated cardiomyopathy than in normal controls (33.7% versus
30.6%). The differences in genotype or allele frequencies of ZBTB17
gene rs10927875 polymorphism were not statistically significant
(p=0.6908; p=0.6098). The results of this study suggest that ZBTB17
gene rs10927875 polymorphism may be a risk factor for
susceptibility to DCM in Slovak patients with DCM. Studies of
numerous files and additional functional investigations are needed to
fully understand the roles of genetic associations.
Abstract: This paper reports a new and accurate method for load-flow solution of radial distribution networks with minimum data preparation. The node and branch numbering need not to be sequential like other available methods. The proposed method does not need sending-node, receiving-node and branch numbers if these are sequential. The proposed method uses the simple equation to compute the voltage magnitude and has the capability to handle composite load modelling. The proposed method uses the set of nodes of feeder, lateral(s) and sub lateral(s). The effectiveness of the proposed method is compared with other methods using two examples. The detailed load-flow results for different kind of load-modellings are also presented.
Abstract: This paper presents a numerical approach for the static
and dynamic analysis of hydrodynamic radial journal bearings. In the
first part, the effect of shaft and housing deformability on pressure
distribution within oil film is investigated. An iterative algorithm that
couples Reynolds equation with a plane finite elements (FE)
structural model is solved. Viscosity-to-pressure dependency (Vogel-
Barus equation) is also included. The deformed lubrication gap and
the overall stress state are obtained. Numerical results are presented
with reference to a typical journal bearing configuration at two
different inlet oil temperatures. Obtained results show the great
influence of bearing components structural deformation on oil
pressure distribution, compared with results for ideally rigid
components. In the second part, a numerical approach based on
perturbation method is used to compute stiffness and damping
matrices, which characterize the journal bearing dynamic behavior.
Abstract: Generally flow behavior in centrifugal fan is observed
to be in a state of instability with flow separation zones on suction
surface as well as near the front shroud. Overall performance of the
diffusion process in a centrifugal fan could be enhanced by
judiciously introducing the boundary layer suction slots. With easy
accessibility of CFD as an analytical tool, an extensive numerical
whole field analysis of the effect of boundary layer suction slots in
discrete regions of suspected separation points is possible. This paper
attempts to explore the effect of boundary layer suction slots
corresponding to various geometrical locations on the impeller with
converging configurations for the slots. The analysis shows that the
converging suction slots located on the impeller blade about 25%
from the trailing edge, significantly improves the static pressure
recovery across the fan. Also it is found that Slots provided at a
radial distance of about 12% from the leading and trailing edges
marginally improve the static pressure recovery across the fan.
Abstract: To minimize power losses, it is important to
determine the location and size of local generators to be placed in
unbalanced power distribution systems. On account of some inherent
features of unbalanced distribution systems, such as radial structure,
large number of nodes, a wide range of X/R ratios, the conventional
techniques developed for the transmission systems generally fail on
the determination of optimum size and location of distributed
generators (DGs). This paper presents a simple method for
investigating the problem of contemporaneously choosing best
location and size of DG in three-phase unbalanced radial distribution
system (URDS) for power loss minimization and to improve the
voltage profile of the system. Best location of the DG is determined
by using voltage index analysis and size of DG is computed by
variational technique algorithm according to available standard size
of DGs. This paper presents the results of simulations for 25-bus and
IEEE 37- bus Unbalanced Radial Distribution system.
Abstract: A new, rapidly convergent, numerical procedure for
internal loading distribution computation in statically loaded, singlerow,
angular-contact ball bearings, subjected to a known combined
radial and thrust load, which must be applied so that to avoid tilting
between inner and outer rings, is used to find the load distribution
differences between a loaded unfitted bearing at room temperature,
and the same loaded bearing with interference fits that might
experience radial temperature gradients between inner and outer
rings. For each step of the procedure it is required the iterative
solution of Z + 2 simultaneous nonlinear equations – where Z is the
number of the balls – to yield exact solution for axial and radial
deflections, and contact angles.
Abstract: This paper focuses on developing an integrated
reliable and sophisticated model for ultra large wind turbines And to
study the performance and analysis of vector control on large wind
turbines. With the advance of power electronics technology, direct
driven multi-pole radial flux PMSG (Permanent Magnet Synchronous
Generator) has proven to be a good choice for wind turbines
manufacturers. To study the wind energy conversion systems, it is
important to develop a wind turbine simulator that is able to produce
realistic and validated conditions that occur in real ultra MW wind
turbines. Three different packages are used to simulate this model,
namely, Turbsim, FAST and Simulink. Turbsim is a Full field wind
simulator developed by National Renewable Energy Laboratory
(NREL). The wind turbine mechanical parts are modeled by FAST
(Fatigue, Aerodynamics, Structures and Turbulence) code which is
also developed by NREL. Simulink is used to model the PMSG, full
scale back to back IGBT converters, and the grid.
Abstract: Injection forging is a Nett-shape manufacturing
process in which one or two punches move axially causing a radial
flow into a die cavity in a form which is prescribed by the exitgeometry,
such as pulley, flanges, gears and splines on a shaft. This
paper presents an experimental and numerical study of the injection
forging of splines in terms of load requirement and material flow.
Three dimensional finite element analyses are used to investigate the
effect of some important parameters in this process. The experiment
has been carried out using solid commercial lead billets with two
different billet diameters and four different dies.
Abstract: This paper presents the applicability of artificial
neural networks for 24 hour ahead solar power generation forecasting
of a 20 kW photovoltaic system, the developed forecasting is suitable
for a reliable Microgrid energy management. In total four neural
networks were proposed, namely: multi-layred perceptron, radial
basis function, recurrent and a neural network ensemble consisting in
ensemble of bagged networks. Forecasting reliability of the proposed
neural networks was carried out in terms forecasting error
performance basing on statistical and graphical methods. The
experimental results showed that all the proposed networks achieved
an acceptable forecasting accuracy. In term of comparison the neural
network ensemble gives the highest precision forecasting comparing
to the conventional networks. In fact, each network of the ensemble
over-fits to some extent and leads to a diversity which enhances the
noise tolerance and the forecasting generalization performance
comparing to the conventional networks.
Abstract: Finding the interpolation function of a given set of nodes is an important problem in scientific computing. In this work a kind of localization is introduced using the radial basis functions which finds a sufficiently smooth solution without consuming large amount of time and computer memory. Some examples will be presented to show the efficiency of the new method.
Abstract: Within the collaborative research center 666 a new
product development approach and the innovative manufacturing
method of linear flow splitting are being developed. So far the design process is supported by 3D-CAD models utilizing User Defined
Features in standard CAD-Systems. This paper now presents new
functions for generating 3D-models of integral sheet metal products with bifurcations using Siemens PLM NX 6. The emphasis is placed
on design and semi-automated insertion of User Defined Features.
Therefore User Defined Features for both, linear flow splitting
and its derivative linear bend splitting, were developed. In order to facilitate the modeling process, an application was developed
that guides through the insertion process. Its usability and dialog layout adapt known standard features. The work presented here has
significant implications on the quality, accurateness and efficiency of the product generation process of sheet metal products with higher
order bifurcations.
Abstract: Monitoring lightning electromagnetic pulses (sferics)
and other terrestrial as well as extraterrestrial transient radiation signals
is of considerable interest for practical and theoretical purposes
in astro- and geophysics as well as meteorology. Managing a continuous
flow of data, automisation of the detection and classification
process is important. Features based on a combination of wavelet and
statistical methods proved efficient for analysis and characterisation
of transients and as input into a radial basis function network that is
trained to discriminate transients from pulse like to wave like.
Abstract: This paper discusses a genetic algorithm (GA) based optimal load shedding that can apply for electrical distribution networks with and without dispersed generators (DG). Also, the proposed method has the ability for considering constant and variable capacity deficiency caused by unscheduled outages in the bulked generation and transmission system of bulked power supply. The genetic algorithm (GA) is employed to search for the optimal load shedding strategy in distribution networks considering DGs in two cases of constant and variable modelling of bulked power supply of distribution networks. Electrical power distribution systems have a radial network and unidirectional power flows. With the advent of dispersed generations, the electrical distribution system has a locally looped network and bidirectional power flows. Therefore, installed DG in the electrical distribution systems can cause operational problems and impact on existing operational schemes. Introduction of DGs in electrical distribution systems has introduced many new issues in operational and planning level. Load shedding as one of operational issue has no exempt. The objective is to minimize the sum of curtailed load and also system losses within the frame-work of system operational and security constraints. The proposed method is tested on a radial distribution system with 33 load points for more practical applications.
Abstract: As every system conceptions the concept of crisis is
based on the system of interdependent elements. These dialectic
elements occur in a majority of definitions even though called
differently. For further theoretical searching but also for practical
utilization it is necessary to understand these elements. The paper
stresses that the concept of crisis is ambiguous. There are identified
and explained the elements that are generally found in most crises
(disruption, precondition, triggers etc).
Abstract: Changes in stem diameter of orchid plants were
investigated in a control growing climate. Previous studies have
focused on stem diameter in relation to plant water on terrestrial
plants in order to schedule the irrigation. The objective of this work
was to evaluate the ability of the strain gauges to capture changes in
the epiphytes plant stem. Experiments were carried out by using the
sympodial orchid, Dendrobium Sonia in a stressed condition. From
the findings, the sensor can detect changes in the plant stem and the
result can easily be used as a reference for further studies for the
development of a proper watering system.
Abstract: This paper describes an automatic algorithm to restore
the shape of three-dimensional (3D) left ventricle (LV) models created
from magnetic resonance imaging (MRI) data using a geometry-driven
optimization approach. Our basic premise is to restore the LV shape
such that the LV epicardial surface is smooth after the restoration. A
geometrical measure known as the Minimum Principle Curvature (κ2)
is used to assess the smoothness of the LV. This measure is used to
construct the objective function of a two-step optimization process.
The objective of the optimization is to achieve a smooth epicardial
shape by iterative in-plane translation of the MRI slices.
Quantitatively, this yields a minimum sum in terms of the magnitude
of κ
2, when κ2 is negative. A limited memory quasi-Newton algorithm,
L-BFGS-B, is used to solve the optimization problem. We tested our
algorithm on an in vitro theoretical LV model and 10 in vivo
patient-specific models which contain significant motion artifacts. The
results show that our method is able to automatically restore the shape
of LV models back to smoothness without altering the general shape of
the model. The magnitudes of in-plane translations are also consistent
with existing registration techniques and experimental findings.
Abstract: This work explores blind image deconvolution by recursive function approximation based on supervised learning of neural networks, under the assumption that a degraded image is linear convolution of an original source image through a linear shift-invariant (LSI) blurring matrix. Supervised learning of neural networks of radial basis functions (RBF) is employed to construct an embedded recursive function within a blurring image, try to extract non-deterministic component of an original source image, and use them to estimate hyper parameters of a linear image degradation model. Based on the estimated blurring matrix, reconstruction of an original source image from a blurred image is further resolved by an annealed Hopfield neural network. By numerical simulations, the proposed novel method is shown effective for faithful estimation of an unknown blurring matrix and restoration of an original source image.